An Application of Porters Stemming Algorithm for Text Mining in Healthcare

Text mining has diverse applications in variety of fields where manual analysis and generating effective knowledge discovery from information is not possible because of huge availability of information on website. There is 90% of web data is in unstructured and semi structured form and only 10% is in structured format. The data analysis and decision making from unstructured and semi structured data arises biggest challenge and research opportunities. The paper is focused towards text mining and text pre-processing aspects. The paper is briefed about the need of stemming for healthcare, stemming of various viral infective diseases information. Also the paper elaborated on comparison of various stemmers. This paper is focused on implementation of porters stemming algorithm for viral diseases information and textual data. The output and results are disused along with further opportunities in stemming technique improvements.